web-service-mlflow

Getting the model for deployment from MLflow

• Take the code from the previous video
• Train another model, register with MLflow
• Put the model into a scikit-learn pipeline
• Model deployment with tracking server
• Model deployment without the tracking server

Starting the MLflow server with S3:

mlflow server \
    --backend-store-uri=sqlite:///mlflow.db \
    --default-artifact-root=s3://mlflow-models-alexey/

Downloading the artifact

export MLFLOW_TRACKING_URI="http://127.0.0.1:5000"
export MODEL_RUN_ID="6dd459b11b4e48dc862f4e1019d166f6"

mlflow artifacts download \
    --run-id ${MODEL_RUN_ID} \
    --artifact-path model \
    --dst-path .